Modelling the surface mass balance of the Greenland ice sheet and neighbouring ice caps: A dynamical and statistical downscaling approach
Bibliographic record
Abstract
The Greenland ice sheet (GrIS) is the world’s second largest ice mass, storing about one tenth of the Earth’s freshwater. If totally melted, global sea level would rise by 7.4 m, affecting low-lying regions worldwide. Since the mid-1990s, increased atmospheric and oceanic temperatures have accelerated GrIS mass loss through increased meltwater runoff and ice discharge from marine-terminating glaciers. To understand the causes of recent GrIS surface mass loss, we use the Regional Atmospheric Climate Model RACMO2. This meteorological model simulates the GrIS surface mass balance (SMB), i.e. the difference between snowfall accumulation and ablation from meltwater runoff. To cover a large domain at reasonable computational cost, RACMO2 is run at a relatively coarse horizontal resolution of 11 km (1958-2016). At this spatial resolution, the model does not well resolve small glaciated bodies, such as narrow glaciers and small peripheral ice caps (GICs), detached from the main ice sheet. Therefore, we developed a statistical downscaling algorithm that reprojects the RACMO2 output on a 1 km grid. This downscaled product allows to quantify mass changes of small ice masses in unprecedented detail. Using the downscaled data set, we identify 1997 as a tipping point for the mass balance of Greenland’s GICs. The GICs are located in relatively dry regions where summer melt nominally exceeds winter snowfall. To sustain these ice caps, the refreezing of meltwater in the snow is a key process. The snow acts as a ”sponge” that buffers a large fraction of meltwater, which subsequently refreezes in winter. The remaining meltwater runs off to the ocean and directly contributes to mass loss. Until 1997, the snow layer in the interior of these GICs could compensate for increased melt by refreezing more meltwater. Around 1997, following decades of increased melt, the snow became saturated with refrozen meltwater, so that any additional summer melt was forced to run off to the ocean, tripling the mass loss. We call this a tipping point, as it would take decades to regrow a new, healthy snow layer that could buffer enough summer meltwater. As a result, Greenland’s GICs are expected to undergo irreversible mass loss in the future. Similar mechanisms are at play in the Canadian Arctic Archipelago. While the northern ice caps, that are larger and more elevated, can still efficiently buffer meltwater in their extensive snow-covered accumulation zones, the southern smaller and lower-lying ice fields have already lost most of their meltwater retention capacity, causing uninterrupted mass loss during the last six decades. Consequently, these southern ice caps are expected to disappear within the next 400 years. For now, the main Greenland ice sheet is still safe, as porous snow in the extensive accumulation zone, covering about 90% of the GrIS, still buffers most of the summer melt. At the current rate of mass loss, it would still take 10,000 years to melt the GrIS completely. However, the tipping point reached for the peripheral GICs must be regarded as an alarm-signal for the GrIS in the near future, if temperatures continue to increase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".